Conventional few-shot segmentation methods only utilized CNN-based feature backbones [1]} for extracting features. [2]} observed that high-level features contains semantics of objects which could lead to overfitting and not suitable to use for the task of few-shot segmentation. Then the question naturally arises. What about transformer-based backbone networks? As addressed in many works [3]}, [4]}, CNN and transformers see images differently, which means that the kinds of backbone networks may affect the performance significantly, but this has never been explored in this task. We thus exploit several well-known vision transformer architectures to explore the potential differences that probably exist.
